● Led investigation of computational rules for CRISPRi guide design using open-source machine learning algorithms.

● Optimized of genome-wide CRISPRi/a transcriptional regulation in cancer cell lines, using epigenomic and genomic data analysis.

● Computational lead on CRISPR screen focused on lysosomal storage and mitochondrial dysfunction in Niemann-Pick Type C Disease.

● Integrated single-cell epigenetic and transcriptomic data to identify gene regulatory networks driving cell differentiation.

● Mentored junior programmers joining computational biology team.

● Quantifed and characterized CRISPR gene modifications, with both custom and open-source tools for genomic data analysis.

● Queried, retrieved, and integrated data from public genomics databases, to enhance on-target gene editing with sequence alignment protocols.

● Examined concordance and sensitivity of five computational CRISPR off-target detection methods.

● Streamlined high-performance analyses for bench scientists via custom data pipelines and user interfaces.

● Established department-first machine learning pipeline to study effects of biomarker operations on quality of cancer immunotherapy assays.

● Upgraded data operations for six data streams, including flow cytometry, biomarker operations, and oncology data warehouses.

● Monitored and solved sample data quality issues for two cancer immunotherapy trial arms.

● Collaborated with AI engineers to create an ETL pipeline for multi-modal survival prediction and patient stratification, by using Python ML and R to integrate gene panel and RNA-Seq data.

● Designed ETL data pipeline for integration of real-world EHRs into deep learning module, using Python, SQL, and Apache Spark.

● Organized and managed close coordination of Artificial Intelligence, data management, and DevOps functions, to bring clinical machine learning from prototype to production.

● Established an integrated genetic and curated literature workflow to predict incidence and prevalence rates of over sixty rare, genetic disorders, with statistical and data visualization R packages (ggplot2) and MATLAB.

● Upgraded and tested RNA-Seq data pipeline (STAR2, SAMtools, Bioconductor) to discern disease contribution of rare variants to neural disease.

● Spearheaded development of three relational databases derived from Hail population genomics platform, in collaboration with software engineer.

● Co-authored manuscript submitted to peer-reviewed publication, on prediction of disease severity in metachromatic leukodystrophy.

● Developed allele-specific association models and GWAS analysis software for two later published manuscripts.

● Mentored research associates in developing bioinformatic skills, specifically in multiple sequence alignment and standard molecular biological protocols (restriction enzyme digestion, gel electrophoresis, spectrophotometry).

● Identified over forty putative conserved vertebrate phosphodiesterase proteins, by using multiple sequence alignment and genomic evidence.

Topics: Introduction to RNA-Seq, Standard Workflows with DESeq2, Reference-Based Assembly, Exploratory Data Analysis, Statistical Analysis, Data Visualization, Gene Set Enrichment, Variant Annotation

● Presented publication-quality RNA-Seq data summaries and representations to post-doctoral associates

General Biology II facilitator for two concurrent PLTL courses